Recognition technology has developed in recent years and is widely used. Particularly, various technologies and products using the recognition technology have been developed. For example, a device for detecting the presence or absence of a specific object using object recognition technology has been developed.
However, the expansion in the application scope of the recognition technology has led to various needs for the recognition technology. Examples of the needs include the precision improvement and the acceleration of recognition processing. Such recognition processing is typically performed using a classifier which extracts a feature value and uses the feature value as an input. Therefore, the result of recognition processing depends on the precision and the accuracy of the processing using the classifier. Therefore, the generation of the classifier is important.
However, with complication of the recognition target, the generation of a classifier becomes more difficult. For example, for the generation of a classifier, machine learning based on learning data is often used. However, a vast amount of learning data may be required to improve the precision and the accuracy of the classifier. Therefore, the time for generating such a classifier can lengthen.
Meanwhile, the generation of a classifier by using transfer learning has been proposed. For example, Patent Literature 1 discloses a technology relating to a learning device that generates an object classifier by using so-called boosting-based transfer learning. Particularly, the learning device acquires an existing classifier as a transfer classifier. Next, the learning device selects a weak classifier having the minimum error rate relating to the feature value that is extracted from learning images among the weak classifiers that constitute the transfer classifier. Then, the learning device generates an object classifier by linearly coupling selected weak classifiers. According to the technology, it is said that the time for generating such an object classifier shortens.